Abstract
BACKGROUND: The ability of the brain to perform multiple complex tasks with fixed structures has yet to be fully elucidated. Structural connectivity (SC) and functional connectivity (FC) have been increasingly used to understand the structure and function of the brain respectively. However, a limited number of studies have explored the relationship between both entities especially in translational animal models. NEW METHOD: We proposed an integration of both SC and FC can improve understanding of brain's structure, function, their interplay, and brain's response to neurological conditions such as traumatic brain injury (TBI). We investigated structure-function correlation at multiple scales (small: cortical regions, medium: resting state networks, and large: hemispheric and whole brain), and adapted a Bayesian framework to incorporate SC for constructing structurally-informed FC (siFC) using a translational porcine model. RESULTS: There is a significantly strong correlation r = 0.277 ± 0.011 between SC and FC in healthy pigs which is consistent across different scales. Further, siFC stability is measured as a Pearson correlation (r = 0.72 ± 0.07) between time-resolved FCs. Subsequent differential degree test analysis using siFC provided more explicit profiling of perturbations caused by TBI. COMPARING WITH EXISTING METHODS: The siFC is more immune to large, dynamic variability than FC alone. A more accurate profiling of significantly altered connections and affected hubs by TBI is achieved which is consistent with TBI induced structural deformations. CONCLUSION: Our findings demonstrated that SC-FC integration model improved detection of significant differences in brain connectivity and pinpoints hub regions that had been directly impacted by TBI.